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- W2892358082 abstract "Robust risk minimisation has several advantages: it has been studied with regards to improving the generalisation properties of models and robustness to adversarial perturbation. We bound the distributionally robust risk for a model class rich enough to include deep neural networks by a regularised empirical risk involving the Lipschitz constant of the model. This allows us to interpretand quantify the robustness properties of a deep neural network. As an application we show the distributionally robust risk upperbounds the adversarial training risk." @default.
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- W2892358082 date "2018-09-04" @default.
- W2892358082 modified "2023-09-27" @default.
- W2892358082 title "Lipschitz Networks and Distributional Robustness" @default.
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